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Tutorial on Microbiome Data Analysis

This tutorial gets You started with R tools for microbial ecology. In particular, to provide an introduction to

The tutorial slides are available here

Installation

Launch R/RStudio and install the microbiome R package (see installation instructions).

To initiate reproducible documentation, do the following in RStudio:

  1. Open a new Rmarkdown (.html) file
  2. Convert that .html file with the ‘knit’ button
  3. Modify the file and knit again to make your own reproducible report

Example data: Intestinal microbiota of 1006 Western adults

Example data set will be the HITChip Atlas, which is available via the microbiome R package in phyloseq format. This data set from Lahti et al. Nat. Comm. 5:4344, 2014 comes with 130 genus-like taxonomic groups across 1006 western adults with no reported health complications. Some subjects have also short time series. Load the data in R with:

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 130 taxa and 1151 samples ]
## sample_data() Sample Data:       [ 1151 samples by 10 sample variables ]
## tax_table()   Taxonomy Table:    [ 130 taxa by 2 taxonomic ranks ]

Phyloseq data stucture for taxonomic profiling

Explore the phyloseq data format. See examples on microbiome data manipulation.

Diversity

Explore the estimation and analysis of various diversity indices and taxonomic composition.

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observedchao1 diversity_inverse_simpson diversity_gini_simpson diversity_shannon diversity_fisher diversity_coveragediversities_inverse_simpson diversities_gini_simpson diversities_shannon diversities_fisher diversities_coveragechao1 diversities_inverse_simpson diversities_gini_simpson diversities_shannon diversities_fisher diversities_coverageevenness_camargo evenness_pielou evenness_simpson evenness_evar evenness_bulla dominance_dbp dominance_dmn dominance_absolute dominance_relative dominance_simpson dominance_core_abundance dominance_gini rarity_log_modulo_skewness rarity_low_abundance rarity_noncore_abundance rarity_rare_abundance
Sample-1 99 108.3750 12.983930 0.9229817 3.187815 16.07126 5 0.3675967 0.3567791 0.1311508 0.1669647 0.3447496 0.1759515 0.3379428 1336 0.1759515 0.0770183 0.9998683 0.8488881 2.059488 0.0264718 0.0001317 0.0001317
Sample-2 98 108.5625 16.595578 0.9397430 3.394462 15.04043 7 0.3437543 0.3679621 0.1693426 0.1483521 0.3958182 0.1716594 0.2821246 1742 0.1716594 0.0602570 1.0000000 0.8188881 2.055369 0.0198069 0.0000000 0.0000000
Sample-3 99 110.2500 8.703493 0.8851036 2.864855 16.26890 4 0.3242763 0.3229022 0.0879141 0.1906035 0.2680793 0.2793437 0.3985416 1992 0.2793437 0.1148964 0.9998598 0.8806975 2.053348 0.0412284 0.0000000 0.0000000
Sample-4 100 111.2500 10.709023 0.9066208 3.056922 15.21763 4 0.3924486 0.3289708 0.1070902 0.1559785 0.3336218 0.1957623 0.3813911 2125 0.1957623 0.0933792 0.9999079 0.8604110 2.057767 0.0247812 0.0000921 0.0000921
Sample-5 98 112.0625 12.248008 0.9183541 3.073742 14.59865 4 0.4112500 0.3272493 0.1249797 0.1429785 0.3194244 0.1686667 0.3334167 2024 0.1686667 0.0816459 0.9999167 0.8672179 2.057402 0.0234167 0.0000000 0.0000000
Sample-6 99 113.0625 9.665190 0.8965359 2.941993 15.94374 3 0.2819936 0.3277479 0.0976282 0.1824172 0.3030473 0.2273187 0.3802123 1799 0.2273187 0.1034641 1.0000000 0.8735717 2.060100 0.0380339 0.0000000 0.0000000

Assign the estimated diversity to sample metadata

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sample_data(pseq)$diversity <- tab$diversity_shannon
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sample_data(pseq)$diversity <- tab$diversities_shannon
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Visualize the data

Technical biases

Explore potential technical biases in the data. DNA extraction method has a remarkable effect on sample grouping.

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# Use relative abundance data
ps <- microbiome::transform(pseq, "compositional")

# Pick core taxa
ps <- core(ps, detection = 0, prevalence = 80/100)

# For this example, choose samples with DNA extraction information available
ps <- subset_samples(ps, !is.na(DNA_extraction_method))

# Illustrate sample similarities with PCoA (NMDS)
plot_landscape(ps, "NMDS", "bray", col = "DNA_extraction_method")

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# Use relative abundance data
ps <- microbiome::transform(pseq, "compositional")

# For this example, choose samples with DNA extraction information available
ps <- subset_samples(ps, !is.na(DNA_extraction_method))

# Or: you could focus on a single DNA extraction method
# ps <- subset_samples(ps, DNA_extraction_method == "r")

# Illustrate sample similarities with PCoA (NMDS)
plot_landscape(ps, "NMDS", "bray", col = "DNA_extraction_method")

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Core microbiota

Core microbiota refers to the set of species shared by (almost) all individuals.

A full phyloseq object with just the core taxa is obtained as follows:

Visualizing the core

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# Core with compositionals:
prevalences <- seq(.05, 1, .05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

p <- plot_core(pseq.rel, plot.type = "heatmap",
    prevalences = prevalences, detections = detections) +
    xlab("Detection Threshold (Relative Abundance)")
print(p)    

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# Core with compositionals:
prevalences <- seq(.05, 1, .05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

p <- plot_core(pseq.rel, plot.type = "heatmap",
    prevalences = prevalences, detections = detections) +
    xlab("Detection Threshold (Relative Abundance)")
print(p)    

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Other tools

Explore further tools in microbiome tutorial.

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Leo Lahti, Sudarshan Shetty et al. 2018-11-19

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Leo Lahti, Sudarshan Shetty et al. 2018-07-22

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Leo Lahti, Sudarshan Shetty et al. 2018-11-15

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